Pass the Microsoft Certified: Azure AI Fundamentals AI-901 Questions and answers with ExamsMirror

Evaluating model outcomes across demographic groups to reduce bias is an example of the Microsoft responsible AI principle of fairness .

The Microsoft responsible AI principle of fairness means AI systems should treat all people fairly and avoid unfair bias or discrimination across groups.

The other options are incorrect:

accountability means people and organizations remain responsible for AI systems and their effects.

privacy and security focuses on protecting data and controlling access.

transparency means users should understand when and how AI is being used, including system capabilities and limitations.

To ensure a vision-enabled model produces a relevant and useful response, the prompt should include a clear description of the task . For example, you should tell the model what to do with the image, such as describe it, identify objects, compare images, extract visible text, or answer a specific question about the visual content.

The other options are incorrect:

B. a dataset ID for the image is not needed in the prompt.

C. the version number of the model is deployment/configuration information, not prompt content.

D. the deployment name of the model is used in the API call to route the request, not inside the prompt.

The requirement is to convert written customer notifications into natural-sounding spoken audio . This is speech synthesis , also known as text to speech .

Microsoft’s Azure Speech documentation describes text to speech as a capability that converts text into natural-sounding synthesized speech. Therefore, for playing written notifications over a phone system, the correct Azure Speech capability is speech synthesis .

Why the other options are incorrect:

A. speaker recognition identifies or verifies speakers by voice.

C. speech recognition converts spoken audio into text.

D. speech translation translates spoken audio between languages.

Statement 1: To process prompts that contain images, you must deploy a multimodal generative AI model. = Yes

Image prompts require a vision-enabled or multimodal model. A text-only model cannot interpret image content.

Statement 2: In the Foundry playground, you can provide an image by uploading a local file or by specifying a publicly accessible URL. = Yes

The Foundry playground for vision-capable models supports adding image input to a prompt, including local uploads and image URLs.

Statement 3: A multimodal model can interpret multiple images included in a single prompt. = Yes

Vision-enabled multimodal models can accept image content items in a prompt, and multiple images can be included for the model to analyze together.

Voicemail recordings are audio content . Azure Content Understanding analyzers define what type of content to process, including documents, images, audio, or video , and what elements to extract, including transcripts and structured fields.

Microsoft’s custom analyzer documentation also shows an audio example based on prebuilt-audio for processing customer support call recordings, which is the same content type as voicemail recordings.

Therefore, to extract a transcript and structured information from voicemail recordings, you should use an audio analyzer .

The system must not discriminate based on gender, race, or age. = Fairness

This requirement maps to fairness because fairness means AI systems should avoid unfair bias and treat people equitably across demographic groups.

Personal data must be visible only to approved users. = Privacy and security

This requirement maps to privacy and security because it focuses on protecting personal data and restricting access to authorized users only.

Automated decision-making processes must be recorded so that approved users can identify why a decision was made. = Transparency

This requirement maps to transparency because recording decision-making processes helps users understand and explain how or why an AI-supported decision was made.

The Microsoft responsible AI principle of inclusiveness means AI systems should be designed to empower and engage everyone, including people with different abilities, languages, and accessibility needs.

Therefore, designing the interface to support multiple languages and screen readers is an example of inclusiveness .

Why the other options are incorrect:

A. Provide explanations about how predictions are generated = Transparency

C. Evaluate model outputs across demographic groups to reduce bias = Fairness

D. Encrypt stored customer data and restrict access by using role-based controls = Privacy and security

Statement 1: An AI generative model is retrained before performing each user request. = No

A generative AI model is not retrained for every user request. During normal use, the model performs inference: it receives input, processes the prompt, and generates a response based on the deployed model.

Statement 2: An AI agent responds by copying and pasting answers stored in a database. = No

An AI agent does not simply copy and paste stored answers. Agents use generative AI models, instructions, context, and optionally tools or connected data sources to reason over user input and produce responses or actions.

Statement 3: An AI agent uses a generative AI model to establish actions based on user input. = Yes

This is correct. An AI agent uses a generative AI model together with instructions and available tools to interpret user input, determine the next action, and generate a response.

The completed sentence is:

After deploying a vision-enabled GPT model in Microsoft Foundry, you can configure an application to send requests to the endpoint of the model .

To call a deployed model from an application, the application must send requests to the deployed model’s endpoint and include the required authentication credentials. The Foundry playground is used for testing interactively, but application code calls the deployed model endpoint.

The other options are incorrect:

evaluation pipeline of model is used to evaluate model performance, not to receive inference requests from an app.

Foundry playground is an interactive testing environment, not the production target for application requests.

training dataset of the model is used for training or fine-tuning, not for sending inference requests.

Therefore, the correct answer is endpoint of the model .